Land grabbing and land concentration: Mapping changing patterns of farmland ownership in three rural municipalities in Saskatchewan, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the 2007-2008 global food crisis there is growing interest in changing patterns of farmland ownership. Utilizing a dataset of the names of all farmland titleholders along with GIS data mapping software, this article demonstrates changes in patterns of land ownership in three rural municipalities (RMs) in Saskatchewan, Canada. A diverse mix of new actors have entered the farmland market in the past decade or two, with some now owning more than 100,000 acres each in the province. Our research reveals a list of the investment companies, pension plans, and large farmer/investor hybrids buying land and also maps investment activity and large land transactions in the three RMs. While 7.8% to 13.1% of the farmland is now owned by “land grabbers”, our study also found a significant rise in land concentration in the hands of farmers when compared to 20 years ago. For example, in one RM the four largest landowners—a mix of farmers and investment companies and farmer/investor hybrids—now own 28% of the land. We then discuss some initial findings concerning the impact changing patterns of land ownership is having on the cohesion and vitality of communities and conclude with a series of questions for further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it